FixedRiskRewardLoss Strategy Analysis

Strategy Number: #6 (6th of 465 strategies)
Strategy Type: Fixed Risk/Reward Ratio Dynamic Stoploss Strategy
Timeframe: 5 minutes (5m)

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1. Strategy Overview

FixedRiskRewardLoss is an example strategy demonstrating how to implement fixed risk/reward ratio dynamic stoploss management through the custom_stoploss() function. The core idea is: calculate initial stoploss based on ATR, then set take-profit target at 3.5 times the initial risk, and adjust stoploss to breakeven after reaching certain profit level.

Core Features

Feature	Description
Entry Conditions	None (example strategy, always enters)
Exit Conditions	No technical exits, relies on dynamic stoploss
Protection	Fixed risk/reward ratio + breakeven stoploss
Timeframe	5 minutes
Dependencies	TA-Lib
Special Features	Risk/reward ratio management, breakeven stoploss

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2. Configuration Analysis

2.1 Base Risk Parameters

# Hard stoploss
stoploss = -0.10  # -10%

# Custom stoploss configuration
use_custom_stoploss = True
custom_info = {
    "risk_reward_ratio": 3.5,      # Risk/reward ratio 3.5:1
    "set_to_break_even_at_profit": 1,  # Breakeven at 1x risk profit
}

Design Logic:

• Risk/Reward Ratio 3.5:1: For every $1 risk, expects $3.5 return
• Breakeven Mechanism: After reaching 1x risk profit, adjust stoploss to breakeven
• Example Nature: Focus on demonstrating money management techniques

2.2 Order Type Configuration

Uses Freqtrade default configuration.

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3. Entry Conditions Explained

3.1 Entry Logic

# Entry conditions
dataframe.loc[:, "buy"] = 1  # Always buy

Logic Analysis:

• Example Strategy: Entry logic is not the focus, simplified to always buy
• Focus on Stoploss: Core of strategy is demonstrating dynamic stoploss management
• Practical Application: Replace with real entry signals for live trading

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4. Exit Logic Explained

4.1 Custom Stoploss Function

def custom_stoploss(self, pair, trade, current_time, current_rate, current_profit, **kwargs) -> float:
    # Get initial stoploss at entry
    initial_sl_abs = open_df["stoploss_rate"]
    initial_sl = initial_sl_abs / current_rate - 1
    
    # Calculate initial risk
    risk_distance = trade.open_rate - initial_sl_abs
    
    # Calculate take-profit target (risk × 3.5)
    reward_distance = risk_distance * 3.5
    take_profit_price_abs = trade.open_rate + reward_distance
    takeprofit_sl = take_profit_price_abs / current_rate - 1
    
    # Calculate breakeven level
    break_even_profit_distance = risk_distance * 1
    break_even_sl = (trade.open_rate * (1 + fee) / current_rate) - 1
    
    # Priority: take-profit > breakeven > initial stoploss
    result = initial_sl
    if current_profit >= break_even_profit_pct:
        result = break_even_sl
    if current_profit >= take_profit_pct:
        result = takeprofit_sl
    
    return result

Working Mechanism:

1. Calculate initial stoploss based on ATR at entry
2. Set take-profit at 3.5x the initial risk distance
3. Move stoploss to breakeven after reaching 1x risk profit
4. Dynamically adjust stoploss based on current profit level

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5. Risk Management Features

5.1 Fixed Risk/Reward Ratio

Core Principle:

• Risk: Distance from entry to initial stoploss
• Reward: Distance from entry to take-profit (3.5x risk)
• Ensures positive expectancy over multiple trades

5.2 Breakeven Stoploss

Mechanism:

• After profit reaches 1x risk amount, move stoploss to breakeven
• Protects capital after trade moves in favor
• Eliminates risk on winning trades

5.3 Hard Stoploss Backup

stoploss = -0.10  # -10%

Purpose: Final backup if custom stoploss fails.

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6. Strategy Strengths and Limitations

✅ Strengths

1. Educational Value: Excellent example of custom stoploss implementation
2. Risk Management: Fixed risk/reward ensures positive expectancy
3. Breakeven Protection: Eliminates risk on winning trades
4. Flexible Framework: Can be adapted with real entry signals
5. ATR-Based: Initial stoploss adapts to market volatility

⚠️ Limitations

1. No Entry Logic: Must add real entry signals for production use
2. No Technical Exits: Relies entirely on stoploss management
3. Example Only: Not intended for direct production use
4. Requires Customization: Needs adaptation for specific strategies

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7. Implementation Guide

7.1 Adding Entry Signals

Replace the always-buy logic with real signals:

# Example: Add RSI oversold entry
dataframe.loc[
    (dataframe["rsi"] < 30),
    "buy",
] = 1

7.2 Adjusting Risk/Reward

Modify based on strategy characteristics:

• Conservative: 2:1 or 3:1 ratio
• Aggressive: 4:1 or 5:1 ratio

7.3 Breakeven Level

Adjust set_to_break_even_at_profit:

• Lower value (0.5): Move to breakeven sooner
• Higher value (2): Allow more room before breakeven

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8. Summary

FixedRiskRewardLoss is an educational strategy demonstrating advanced stoploss management. Its core value lies in:

1. Custom Stoploss Example: Shows how to implement custom_stoploss()
2. Risk/Reward Framework: Fixed ratio ensures positive expectancy
3. Breakeven Mechanism: Protects profits on winning trades
4. Adaptable Design: Can be customized with real entry signals

For quantitative traders, this is an excellent learning template for:

• Understanding custom stoploss implementation
• Learning risk/reward ratio management
• Implementing breakeven stoploss logic
• Building robust risk management systems

Recommendations:

• Study the custom_stoploss() implementation carefully
• Add real entry signals for production use
• Adjust risk/reward ratio based on strategy characteristics
• Test thoroughly before live deployment

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